CS 536 Schedule (Tentative)

Note: 2pp = 2 slides per page (portrait), 4pp = 4 slides per page (landscape).
Week Monday Wednesday Friday
Week #1
Jan 7 - 11
Lecture: Overview (2pp, 4pp), Directed Graphical Models (Introduction) (2pp, 4pp)
Assignment #1 out
Lecture: Directed Graphical Models (Reasoning Patterns, Independencies) (2pp, 4pp)
Reading: Section 3.2.1.2, 3.2.2, 3.2.3
Lecture: Directed Graphical Models (D-separation) (2pp, 4pp)
Reading: Section 3.3
Week #2
Jan 14 - 18
Lecture: Directed Graphical Models (I-equivalences, Distributions and Graphs) (2pp, 4pp)
Genie example (HMM.xdsl)
Assignment #2
Assignment #1 due
Reading: Section 3.4 (up to 3.4.3)
Lecture: Undirected Graphical Models (Introduction to Markov Networks) (2pp, 4pp)
Reading: 4.1, 4.2
Lecture: Undirected Graphical Models (Independencies) (2pp, 4pp)
Reading: 4.3
Week #3
Jan 21 - 25
No class (MLK day) Lecture: Undirected Graphical Models (Chordal graphs) (2pp, 4pp)
Reading: 4.4
Assignment #2 due
Assignment #3 out
Lecture: Boltzman Machines (2pp, 4pp)
Week #4
Jan 28 - Feb 1
Lecture: Exact Inference (Enumeration, Variable Elimination) (2pp, 4pp)
Reading: Chapter 9.1-9.3
Lecture: Exact Inference (Variable Elimination) (2pp, 4pp)
Reading: Chapter 9.3
Assignment #3 due
Lecture: Exact Inference (Complexity of Variable Elimination) (2pp, 4pp)
Reading: 9.3
Week #5
Feb 4 - 8
Lecture: Exact Inference (Message Passing) (2pp, 4pp)
Reading: 10.1, 10.2
Practice Midterm, Practice Midterm Solutions
Lecture: Exact Inference (Message Passing 2) (2pp, 4pp) No Class
Week #6
Feb 11 - 15
Midterm Lecture: Approximate Inference (Sampling, Likelihood Weighting) (2pp, 4pp) Midterms returned
Project proposal due
Week #7
Feb 18 - 22
Lecture: Approximate Inference 2 (Importance Sampling) (2pp, 4pp)
Assignment #4 out
Reading: 12.2
Lecture: Monte Carlo Markov Chain 1 (2pp, 4pp)
Reading: Chapter 12.3
Lecture: Monte Carlo Markov Chain 2 (2pp, 4pp)
Reading: Chapter 12.3
Week #8
Feb 25 - Mar 1
Snow day Snow day Lecture: Parameter Learning 1 (2pp, 4pp)
Assignment #4 due
Project Checkpoint
Week #9
Mar 4 - 8
Lecture: Parameter Learning 2 (2pp, 4pp) Lecture: Structure Learning (Chow Liu Trees) (2pp, 4pp)
Assignment #5 out
Lecture: Structure Learning (Constraint-Based Approaches) (2pp, 4pp)
Reading: Chapter 3.4.3, 18.1-18.2
Week #10
Mar 11 - 15
Lecture: Structure Learning (Structure Scoring) (2pp, 4pp)
Reading: 18.3, 18.4.3
Lecture: Variational Inference (2pp, 4pp)
Assignment #5 due
Consultation session for projects

Projects are due Mar 20, 2019 at 5:00 pm. Please slide the writeup under my door (or email the pdf) and submit the code via the TEACH handin.